Overview

Dataset statistics

Number of variables24
Number of observations746
Missing cells3734
Missing cells (%)20.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.0 KiB
Average record size in memory192.2 B

Variable types

Numeric6
Categorical18

Warnings

dl_applied has constant value "D/L" Constant
overs has constant value "20" Constant
date has a high cardinality: 538 distinct values High cardinality
player_of_match has a high cardinality: 223 distinct values High cardinality
id is highly correlated with seasonHigh correlation
season is highly correlated with idHigh correlation
first_bat_score is highly correlated with second_bat_scoreHigh correlation
second_bat_score is highly correlated with first_bat_scoreHigh correlation
id is highly correlated with seasonHigh correlation
season is highly correlated with idHigh correlation
first_bat_score is highly correlated with second_bat_scoreHigh correlation
second_bat_score is highly correlated with first_bat_scoreHigh correlation
id is highly correlated with seasonHigh correlation
season is highly correlated with idHigh correlation
first_bat_score is highly correlated with second_bat_scoreHigh correlation
second_bat_score is highly correlated with first_bat_scoreHigh correlation
id is highly correlated with city and 11 other fieldsHigh correlation
city is highly correlated with id and 10 other fieldsHigh correlation
toss_decision is highly correlated with eliminatorHigh correlation
team2 is highly correlated with id and 6 other fieldsHigh correlation
result is highly correlated with umpire1 and 6 other fieldsHigh correlation
umpire1 is highly correlated with id and 7 other fieldsHigh correlation
eliminator is highly correlated with id and 13 other fieldsHigh correlation
team1 is highly correlated with id and 11 other fieldsHigh correlation
first_bat_score is highly correlated with result and 2 other fieldsHigh correlation
winner is highly correlated with id and 8 other fieldsHigh correlation
first_bowl_team is highly correlated with id and 10 other fieldsHigh correlation
toss_winner is highly correlated with id and 9 other fieldsHigh correlation
season is highly correlated with id and 11 other fieldsHigh correlation
venue is highly correlated with id and 10 other fieldsHigh correlation
second_bat_score is highly correlated with result and 2 other fieldsHigh correlation
first_bat_team is highly correlated with id and 9 other fieldsHigh correlation
umpire2 is highly correlated with id and 8 other fieldsHigh correlation
city has 13 (1.7%) missing values Missing
winner has 12 (1.6%) missing values Missing
eliminator has 738 (98.9%) missing values Missing
dl_applied has 727 (97.5%) missing values Missing
win_by_runs has 410 (55.0%) missing values Missing
win_by_wickets has 348 (46.6%) missing values Missing
result has 734 (98.4%) missing values Missing
umpire3 has 744 (99.7%) missing values Missing
date is uniformly distributed Uniform
umpire3 is uniformly distributed Uniform
id has unique values Unique

Reproduction

Analysis started2021-09-03 02:21:07.836504
Analysis finished2021-09-03 02:21:16.422106
Duration8.59 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct746
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean713552
Minimum335982
Maximum1178425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2021-09-02T21:21:16.548000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum335982
5-th percentile336019.25
Q1501209.25
median598048.5
Q3980984.5
95-th percentile1175367.75
Maximum1178425
Range842443
Interquartile range (IQR)479775.25

Descriptive statistics

Standard deviation284541.8848
Coefficient of variation (CV)0.3987682534
Kurtosis-1.339679771
Mean713552
Median Absolute Deviation (MAD)205839
Skewness0.3531419385
Sum532309792
Variance8.096408419 × 1010
MonotonicityNot monotonic
2021-09-02T21:21:16.676271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5980161
 
0.1%
5483811
 
0.1%
10826071
 
0.1%
5012391
 
0.1%
10825971
 
0.1%
4191611
 
0.1%
9810171
 
0.1%
9810091
 
0.1%
5980011
 
0.1%
7292891
 
0.1%
Other values (736)736
98.7%
ValueCountFrequency (%)
3359821
0.1%
3359831
0.1%
3359841
0.1%
3359851
0.1%
3359861
0.1%
3359871
0.1%
3359881
0.1%
3359891
0.1%
3359901
0.1%
3359911
0.1%
ValueCountFrequency (%)
11784251
0.1%
11784241
0.1%
11784231
0.1%
11784221
0.1%
11784211
0.1%
11784201
0.1%
11784191
0.1%
11784181
0.1%
11784171
0.1%
11784161
0.1%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.369973
Minimum2008
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2021-09-02T21:21:16.784091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12011
median2013
Q32016
95-th percentile2019
Maximum2019
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.326906256
Coefficient of variation (CV)0.00165240681
Kurtosis-1.127006194
Mean2013.369973
Median Absolute Deviation (MAD)3
Skewness0.06805433152
Sum1501974
Variance11.06830523
MonotonicityNot monotonic
2021-09-02T21:21:16.868083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
201376
10.2%
201274
9.9%
201173
9.8%
201060
8.0%
201460
8.0%
201660
8.0%
201860
8.0%
201559
7.9%
201759
7.9%
200858
7.8%
Other values (2)107
14.3%
ValueCountFrequency (%)
200858
7.8%
200957
7.6%
201060
8.0%
201173
9.8%
201274
9.9%
201376
10.2%
201460
8.0%
201559
7.9%
201660
8.0%
201759
7.9%
ValueCountFrequency (%)
201950
6.7%
201860
8.0%
201759
7.9%
201660
8.0%
201559
7.9%
201460
8.0%
201376
10.2%
201274
9.9%
201173
9.8%
201060
8.0%

city
Categorical

HIGH CORRELATION
MISSING

Distinct30
Distinct (%)4.1%
Missing13
Missing (%)1.7%
Memory size6.0 KiB
Mumbai
99 
Kolkata
77 
Delhi
73 
Bangalore
66 
Hyderabad
63 
Other values (25)
355 

Length

Max length14
Median length7
Mean length7.357435198
Min length4

Characters and Unicode

Total characters5393
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowDelhi
3rd rowMumbai
4th rowPune
5th rowMumbai

Common Values

ValueCountFrequency (%)
Mumbai99
13.3%
Kolkata77
10.3%
Delhi73
9.8%
Bangalore66
8.8%
Hyderabad63
8.4%
Chennai56
 
7.5%
Chandigarh54
 
7.2%
Jaipur47
 
6.3%
Pune38
 
5.1%
Durban15
 
2.0%
Other values (20)145
19.4%

Length

2021-09-02T21:21:17.087962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai99
13.1%
kolkata77
10.2%
delhi73
9.6%
bangalore66
 
8.7%
hyderabad63
 
8.3%
chennai56
 
7.4%
chandigarh54
 
7.1%
jaipur47
 
6.2%
pune38
 
5.0%
durban15
 
2.0%
Other values (24)169
22.3%

Most occurring characters

ValueCountFrequency (%)
a923
17.1%
i389
 
7.2%
n386
 
7.2%
e374
 
6.9%
r319
 
5.9%
h303
 
5.6%
u272
 
5.0%
l250
 
4.6%
b221
 
4.1%
d216
 
4.0%
Other values (30)1740
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4612
85.5%
Uppercase Letter757
 
14.0%
Space Separator24
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a923
20.0%
i389
8.4%
n386
8.4%
e374
 
8.1%
r319
 
6.9%
h303
 
6.6%
u272
 
5.9%
l250
 
5.4%
b221
 
4.8%
d216
 
4.7%
Other values (13)959
20.8%
Uppercase Letter
ValueCountFrequency (%)
C136
18.0%
D104
13.7%
M99
13.1%
K89
11.8%
B81
10.7%
H63
8.3%
J55
7.3%
P45
 
5.9%
R23
 
3.0%
A19
 
2.5%
Other values (6)43
 
5.7%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5369
99.6%
Common24
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a923
17.2%
i389
 
7.2%
n386
 
7.2%
e374
 
7.0%
r319
 
5.9%
h303
 
5.6%
u272
 
5.1%
l250
 
4.7%
b221
 
4.1%
d216
 
4.0%
Other values (29)1716
32.0%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a923
17.1%
i389
 
7.2%
n386
 
7.2%
e374
 
6.9%
r319
 
5.9%
h303
 
5.6%
u272
 
5.0%
l250
 
4.6%
b221
 
4.1%
d216
 
4.0%
Other values (30)1740
32.3%

date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct538
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2012-05-06
 
2
2009-04-26
 
2
2015-05-04
 
2
2016-05-15
 
2
2017-05-01
 
2
Other values (533)
736 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7460
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique330 ?
Unique (%)44.2%

Sample

1st row2010-04-24
2nd row2010-03-29
3rd row2008-05-16
4th row2016-04-22
5th row2010-04-22

Common Values

ValueCountFrequency (%)
2012-05-062
 
0.3%
2009-04-262
 
0.3%
2015-05-042
 
0.3%
2016-05-152
 
0.3%
2017-05-012
 
0.3%
2014-05-252
 
0.3%
2012-05-192
 
0.3%
2013-05-012
 
0.3%
2014-05-192
 
0.3%
2010-03-162
 
0.3%
Other values (528)726
97.3%

Length

2021-09-02T21:21:17.312169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-05-062
 
0.3%
2009-04-262
 
0.3%
2015-05-042
 
0.3%
2016-05-152
 
0.3%
2017-05-012
 
0.3%
2014-05-252
 
0.3%
2012-05-192
 
0.3%
2013-05-012
 
0.3%
2014-05-192
 
0.3%
2010-03-162
 
0.3%
Other values (528)726
97.3%

Most occurring characters

ValueCountFrequency (%)
01936
26.0%
-1492
20.0%
21136
15.2%
11069
14.3%
4500
 
6.7%
5469
 
6.3%
3212
 
2.8%
8193
 
2.6%
9185
 
2.5%
7140
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5968
80.0%
Dash Punctuation1492
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01936
32.4%
21136
19.0%
11069
17.9%
4500
 
8.4%
5469
 
7.9%
3212
 
3.6%
8193
 
3.2%
9185
 
3.1%
7140
 
2.3%
6128
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
-1492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01936
26.0%
-1492
20.0%
21136
15.2%
11069
14.3%
4500
 
6.7%
5469
 
6.3%
3212
 
2.8%
8193
 
2.6%
9185
 
2.5%
7140
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII7460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01936
26.0%
-1492
20.0%
21136
15.2%
11069
14.3%
4500
 
6.7%
5469
 
6.3%
3212
 
2.8%
8193
 
2.6%
9185
 
2.5%
7140
 
1.9%

team1
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Royal Challengers Bangalore
97 
Chennai Super Kings
89 
Kolkata Knight Riders
85 
Mumbai Indians
84 
Kings XI Punjab
84 
Other values (10)
307 

Length

Max length27
Median length16
Mean length18.16756032
Min length13

Characters and Unicode

Total characters13553
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowDelhi Daredevils
3rd rowMumbai Indians
4th rowRising Pune Supergiants
5th rowChennai Super Kings

Common Values

ValueCountFrequency (%)
Royal Challengers Bangalore97
13.0%
Chennai Super Kings89
11.9%
Kolkata Knight Riders85
11.4%
Mumbai Indians84
11.3%
Kings XI Punjab84
11.3%
Delhi Daredevils83
11.1%
Rajasthan Royals66
8.8%
Sunrisers Hyderabad53
7.1%
Deccan Chargers39
5.2%
Pune Warriors23
 
3.1%
Other values (5)43
5.8%

Length

2021-09-02T21:21:17.519309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings173
 
9.3%
royal97
 
5.2%
bangalore97
 
5.2%
challengers97
 
5.2%
delhi89
 
4.8%
chennai89
 
4.8%
super89
 
4.8%
knight85
 
4.6%
riders85
 
4.6%
kolkata85
 
4.6%
Other values (22)882
47.2%

Most occurring characters

ValueCountFrequency (%)
a1525
 
11.3%
1122
 
8.3%
n1121
 
8.3%
e1058
 
7.8%
i919
 
6.8%
s879
 
6.5%
r801
 
5.9%
l724
 
5.3%
g519
 
3.8%
h472
 
3.5%
Other values (27)4413
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10479
77.3%
Uppercase Letter1952
 
14.4%
Space Separator1122
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1525
14.6%
n1121
10.7%
e1058
10.1%
i919
8.8%
s879
8.4%
r801
 
7.6%
l724
 
6.9%
g519
 
5.0%
h472
 
4.5%
o391
 
3.7%
Other values (11)2070
19.8%
Uppercase Letter
ValueCountFrequency (%)
K357
18.3%
R328
16.8%
C231
11.8%
D211
10.8%
I168
8.6%
S156
8.0%
P121
 
6.2%
B97
 
5.0%
M84
 
4.3%
X84
 
4.3%
Other values (5)115
 
5.9%
Space Separator
ValueCountFrequency (%)
1122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12431
91.7%
Common1122
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1525
 
12.3%
n1121
 
9.0%
e1058
 
8.5%
i919
 
7.4%
s879
 
7.1%
r801
 
6.4%
l724
 
5.8%
g519
 
4.2%
h472
 
3.8%
o391
 
3.1%
Other values (26)4022
32.4%
Common
ValueCountFrequency (%)
1122
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1525
 
11.3%
1122
 
8.3%
n1121
 
8.3%
e1058
 
7.8%
i919
 
6.8%
s879
 
6.5%
r801
 
5.9%
l724
 
5.3%
g519
 
3.8%
h472
 
3.5%
Other values (27)4413
32.6%

team2
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Mumbai Indians
99 
Kolkata Knight Riders
91 
Kings XI Punjab
90 
Royal Challengers Bangalore
82 
Rajasthan Royals
80 
Other values (10)
304 

Length

Max length27
Median length16
Mean length17.88739946
Min length13

Characters and Unicode

Total characters13344
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeccan Chargers
2nd rowKolkata Knight Riders
3rd rowKolkata Knight Riders
4th rowRoyal Challengers Bangalore
5th rowDeccan Chargers

Common Values

ValueCountFrequency (%)
Mumbai Indians99
13.3%
Kolkata Knight Riders91
12.2%
Kings XI Punjab90
12.1%
Royal Challengers Bangalore82
11.0%
Rajasthan Royals80
10.7%
Delhi Daredevils78
10.5%
Chennai Super Kings71
9.5%
Sunrisers Hyderabad52
7.0%
Deccan Chargers36
 
4.8%
Pune Warriors23
 
3.1%
Other values (5)44
5.9%

Length

2021-09-02T21:21:17.734883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings161
 
8.7%
mumbai99
 
5.4%
indians99
 
5.4%
kolkata91
 
4.9%
knight91
 
4.9%
riders91
 
4.9%
xi90
 
4.9%
punjab90
 
4.9%
delhi85
 
4.6%
royal82
 
4.4%
Other values (22)870
47.1%

Most occurring characters

ValueCountFrequency (%)
a1538
 
11.5%
1103
 
8.3%
n1099
 
8.2%
e965
 
7.2%
i926
 
6.9%
s892
 
6.7%
r745
 
5.6%
l676
 
5.1%
g484
 
3.6%
h452
 
3.4%
Other values (27)4464
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10302
77.2%
Uppercase Letter1939
 
14.5%
Space Separator1103
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1538
14.9%
n1099
10.7%
e965
9.4%
i926
9.0%
s892
8.7%
r745
 
7.2%
l676
 
6.6%
g484
 
4.7%
h452
 
4.4%
u388
 
3.8%
Other values (11)2137
20.7%
Uppercase Letter
ValueCountFrequency (%)
K357
18.4%
R349
18.0%
D199
10.3%
C196
10.1%
I189
9.7%
S139
 
7.2%
P129
 
6.7%
M99
 
5.1%
X90
 
4.6%
B82
 
4.2%
Other values (5)110
 
5.7%
Space Separator
ValueCountFrequency (%)
1103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12241
91.7%
Common1103
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1538
 
12.6%
n1099
 
9.0%
e965
 
7.9%
i926
 
7.6%
s892
 
7.3%
r745
 
6.1%
l676
 
5.5%
g484
 
4.0%
h452
 
3.7%
u388
 
3.2%
Other values (26)4076
33.3%
Common
ValueCountFrequency (%)
1103
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1538
 
11.5%
1103
 
8.3%
n1099
 
8.2%
e965
 
7.2%
i926
 
6.9%
s892
 
6.7%
r745
 
5.6%
l676
 
5.1%
g484
 
3.6%
h452
 
3.4%
Other values (27)4464
33.5%

toss_winner
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Mumbai Indians
95 
Kolkata Knight Riders
91 
Chennai Super Kings
87 
Royal Challengers Bangalore
80 
Delhi Daredevils
80 
Other values (10)
313 

Length

Max length27
Median length16
Mean length17.89678284
Min length13

Characters and Unicode

Total characters13351
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeccan Chargers
2nd rowDelhi Daredevils
3rd rowMumbai Indians
4th rowRising Pune Supergiants
5th rowChennai Super Kings

Common Values

ValueCountFrequency (%)
Mumbai Indians95
12.7%
Kolkata Knight Riders91
12.2%
Chennai Super Kings87
11.7%
Royal Challengers Bangalore80
10.7%
Delhi Daredevils80
10.7%
Kings XI Punjab80
10.7%
Rajasthan Royals79
10.6%
Sunrisers Hyderabad46
6.2%
Deccan Chargers43
5.8%
Pune Warriors20
 
2.7%
Other values (5)45
6.0%

Length

2021-09-02T21:21:17.943052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings167
 
9.0%
mumbai95
 
5.1%
indians95
 
5.1%
kolkata91
 
4.9%
knight91
 
4.9%
riders91
 
4.9%
delhi89
 
4.8%
super87
 
4.7%
chennai87
 
4.7%
bangalore80
 
4.3%
Other values (22)878
47.4%

Most occurring characters

ValueCountFrequency (%)
a1530
 
11.5%
1105
 
8.3%
n1104
 
8.3%
e994
 
7.4%
i932
 
7.0%
s886
 
6.6%
r746
 
5.6%
l676
 
5.1%
g487
 
3.6%
h477
 
3.6%
Other values (27)4414
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10315
77.3%
Uppercase Letter1931
 
14.5%
Space Separator1105
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1530
14.8%
n1104
10.7%
e994
9.6%
i932
9.0%
s886
8.6%
r746
 
7.2%
l676
 
6.6%
g487
 
4.7%
h477
 
4.6%
u377
 
3.7%
Other values (11)2106
20.4%
Uppercase Letter
ValueCountFrequency (%)
K365
18.9%
R342
17.7%
C219
11.3%
D212
11.0%
I175
9.1%
S146
 
7.6%
P113
 
5.9%
M95
 
4.9%
X80
 
4.1%
B80
 
4.1%
Other values (5)104
 
5.4%
Space Separator
ValueCountFrequency (%)
1105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12246
91.7%
Common1105
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1530
 
12.5%
n1104
 
9.0%
e994
 
8.1%
i932
 
7.6%
s886
 
7.2%
r746
 
6.1%
l676
 
5.5%
g487
 
4.0%
h477
 
3.9%
u377
 
3.1%
Other values (26)4037
33.0%
Common
ValueCountFrequency (%)
1105
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1530
 
11.5%
1105
 
8.3%
n1104
 
8.3%
e994
 
7.4%
i932
 
7.0%
s886
 
6.6%
r746
 
5.6%
l676
 
5.1%
g487
 
3.6%
h477
 
3.6%
Other values (27)4414
33.1%

toss_decision
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
field
457 
bat
289 

Length

Max length5
Median length5
Mean length4.225201072
Min length3

Characters and Unicode

Total characters3152
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbat
2nd rowbat
3rd rowfield
4th rowfield
5th rowbat

Common Values

ValueCountFrequency (%)
field457
61.3%
bat289
38.7%

Length

2021-09-02T21:21:18.142229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-02T21:21:18.208716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
field457
61.3%
bat289
38.7%

Most occurring characters

ValueCountFrequency (%)
f457
14.5%
i457
14.5%
e457
14.5%
l457
14.5%
d457
14.5%
b289
9.2%
a289
9.2%
t289
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3152
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f457
14.5%
i457
14.5%
e457
14.5%
l457
14.5%
d457
14.5%
b289
9.2%
a289
9.2%
t289
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin3152
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f457
14.5%
i457
14.5%
e457
14.5%
l457
14.5%
d457
14.5%
b289
9.2%
a289
9.2%
t289
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f457
14.5%
i457
14.5%
e457
14.5%
l457
14.5%
d457
14.5%
b289
9.2%
a289
9.2%
t289
9.2%

winner
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)2.0%
Missing12
Missing (%)1.6%
Memory size6.0 KiB
Mumbai Indians
104 
Chennai Super Kings
99 
Kolkata Knight Riders
91 
Royal Challengers Bangalore
82 
Kings XI Punjab
79 
Other values (10)
279 

Length

Max length27
Median length16
Mean length18.09673025
Min length13

Characters and Unicode

Total characters13283
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowDelhi Daredevils
3rd rowMumbai Indians
4th rowRoyal Challengers Bangalore
5th rowChennai Super Kings

Common Values

ValueCountFrequency (%)
Mumbai Indians104
13.9%
Chennai Super Kings99
13.3%
Kolkata Knight Riders91
12.2%
Royal Challengers Bangalore82
11.0%
Kings XI Punjab79
10.6%
Rajasthan Royals73
9.8%
Delhi Daredevils67
9.0%
Sunrisers Hyderabad57
7.6%
Deccan Chargers29
 
3.9%
Gujarat Lions13
 
1.7%
Other values (5)40
 
5.4%

Length

2021-09-02T21:21:18.400691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings178
 
9.7%
mumbai104
 
5.7%
indians104
 
5.7%
chennai99
 
5.4%
super99
 
5.4%
kolkata91
 
4.9%
knight91
 
4.9%
riders91
 
4.9%
royal82
 
4.5%
bangalore82
 
4.5%
Other values (22)819
44.5%

Most occurring characters

ValueCountFrequency (%)
a1506
 
11.3%
n1147
 
8.6%
1106
 
8.3%
e969
 
7.3%
i948
 
7.1%
s875
 
6.6%
r726
 
5.5%
l646
 
4.9%
g492
 
3.7%
h454
 
3.4%
Other values (27)4414
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10258
77.2%
Uppercase Letter1919
 
14.4%
Space Separator1106
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1506
14.7%
n1147
11.2%
e969
9.4%
i948
9.2%
s875
8.5%
r726
 
7.1%
l646
 
6.3%
g492
 
4.8%
h454
 
4.4%
u400
 
3.9%
Other values (11)2095
20.4%
Uppercase Letter
ValueCountFrequency (%)
K372
19.4%
R334
17.4%
C217
11.3%
I183
9.5%
S171
8.9%
D170
8.9%
P106
 
5.5%
M104
 
5.4%
B82
 
4.3%
X79
 
4.1%
Other values (5)101
 
5.3%
Space Separator
ValueCountFrequency (%)
1106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12177
91.7%
Common1106
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1506
 
12.4%
n1147
 
9.4%
e969
 
8.0%
i948
 
7.8%
s875
 
7.2%
r726
 
6.0%
l646
 
5.3%
g492
 
4.0%
h454
 
3.7%
u400
 
3.3%
Other values (26)4014
33.0%
Common
ValueCountFrequency (%)
1106
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1506
 
11.3%
n1147
 
8.6%
1106
 
8.3%
e969
 
7.3%
i948
 
7.1%
s875
 
6.6%
r726
 
5.5%
l646
 
4.9%
g492
 
3.7%
h454
 
3.4%
Other values (27)4414
33.2%

eliminator
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)75.0%
Missing738
Missing (%)98.9%
Memory size6.0 KiB
Rajasthan Royals
Kings XI Punjab
Sunrisers Hyderabad
Royal Challengers Bangalore
Delhi Capitals

Length

Max length27
Median length15.5
Mean length17
Min length14

Characters and Unicode

Total characters136
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)50.0%

Sample

1st rowKings XI Punjab
2nd rowRoyal Challengers Bangalore
3rd rowRajasthan Royals
4th rowSunrisers Hyderabad
5th rowDelhi Capitals

Common Values

ValueCountFrequency (%)
Rajasthan Royals2
 
0.3%
Kings XI Punjab2
 
0.3%
Sunrisers Hyderabad1
 
0.1%
Royal Challengers Bangalore1
 
0.1%
Delhi Capitals1
 
0.1%
Mumbai Indians1
 
0.1%
(Missing)738
98.9%

Length

2021-09-02T21:21:18.604191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-02T21:21:18.675797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
xi2
10.5%
royals2
10.5%
kings2
10.5%
rajasthan2
10.5%
punjab2
10.5%
mumbai1
 
5.3%
indians1
 
5.3%
challengers1
 
5.3%
delhi1
 
5.3%
capitals1
 
5.3%
Other values (4)4
21.1%

Most occurring characters

ValueCountFrequency (%)
a20
14.7%
n11
 
8.1%
s11
 
8.1%
11
 
8.1%
l8
 
5.9%
i7
 
5.1%
e6
 
4.4%
R5
 
3.7%
r5
 
3.7%
g4
 
2.9%
Other values (20)48
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter104
76.5%
Uppercase Letter21
 
15.4%
Space Separator11
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a20
19.2%
n11
10.6%
s11
10.6%
l8
 
7.7%
i7
 
6.7%
e6
 
5.8%
r5
 
4.8%
g4
 
3.8%
u4
 
3.8%
j4
 
3.8%
Other values (8)24
23.1%
Uppercase Letter
ValueCountFrequency (%)
R5
23.8%
I3
14.3%
K2
 
9.5%
X2
 
9.5%
P2
 
9.5%
C2
 
9.5%
B1
 
4.8%
S1
 
4.8%
H1
 
4.8%
D1
 
4.8%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin125
91.9%
Common11
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a20
16.0%
n11
 
8.8%
s11
 
8.8%
l8
 
6.4%
i7
 
5.6%
e6
 
4.8%
R5
 
4.0%
r5
 
4.0%
g4
 
3.2%
u4
 
3.2%
Other values (19)44
35.2%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a20
14.7%
n11
 
8.1%
s11
 
8.1%
11
 
8.1%
l8
 
5.9%
i7
 
5.1%
e6
 
4.4%
R5
 
3.7%
r5
 
3.7%
g4
 
2.9%
Other values (20)48
35.3%

dl_applied
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)5.3%
Missing727
Missing (%)97.5%
Memory size6.0 KiB
D/L
19 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD/L
2nd rowD/L
3rd rowD/L
4th rowD/L
5th rowD/L

Common Values

ValueCountFrequency (%)
D/L19
 
2.5%
(Missing)727
97.5%

Length

2021-09-02T21:21:18.856096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-02T21:21:18.914692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
d/l19
100.0%

Most occurring characters

ValueCountFrequency (%)
D19
33.3%
/19
33.3%
L19
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter38
66.7%
Other Punctuation19
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D19
50.0%
L19
50.0%
Other Punctuation
ValueCountFrequency (%)
/19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin38
66.7%
Common19
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
D19
50.0%
L19
50.0%
Common
ValueCountFrequency (%)
/19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII57
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D19
33.3%
/19
33.3%
L19
33.3%

win_by_runs
Real number (ℝ≥0)

MISSING

Distinct88
Distinct (%)26.2%
Missing410
Missing (%)55.0%
Infinite0
Infinite (%)0.0%
Mean29.88392857
Minimum1
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2021-09-02T21:21:18.983435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q339
95-th percentile86
Maximum146
Range145
Interquartile range (IQR)28

Descriptive statistics

Standard deviation27.27682704
Coefficient of variation (CV)0.9127590762
Kurtosis3.448360412
Mean29.88392857
Median Absolute Deviation (MAD)13
Skewness1.757294972
Sum10041
Variance744.0252932
MonotonicityNot monotonic
2021-09-02T21:21:19.270063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1413
 
1.7%
411
 
1.5%
1011
 
1.5%
99
 
1.2%
139
 
1.2%
239
 
1.2%
79
 
1.2%
19
 
1.2%
229
 
1.2%
159
 
1.2%
Other values (78)238
31.9%
(Missing)410
55.0%
ValueCountFrequency (%)
19
1.2%
27
0.9%
35
0.7%
411
1.5%
56
0.8%
67
0.9%
79
1.2%
85
0.7%
99
1.2%
1011
1.5%
ValueCountFrequency (%)
1461
0.1%
1441
0.1%
1401
0.1%
1381
0.1%
1301
0.1%
1181
0.1%
1111
0.1%
1051
0.1%
1021
0.1%
981
0.1%

win_by_wickets
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)2.5%
Missing348
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean6.251256281
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2021-09-02T21:21:19.377996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q38
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.821280843
Coefficient of variation (CV)0.2913463728
Kurtosis-0.2611319693
Mean6.251256281
Median Absolute Deviation (MAD)1
Skewness-0.1604678764
Sum2488
Variance3.317063909
MonotonicityNot monotonic
2021-09-02T21:21:19.458965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
682
 
11.0%
779
 
10.6%
570
 
9.4%
854
 
7.2%
440
 
5.4%
936
 
4.8%
318
 
2.4%
1011
 
1.5%
25
 
0.7%
13
 
0.4%
(Missing)348
46.6%
ValueCountFrequency (%)
13
 
0.4%
25
 
0.7%
318
 
2.4%
440
5.4%
570
9.4%
682
11.0%
779
10.6%
854
7.2%
936
4.8%
1011
 
1.5%
ValueCountFrequency (%)
1011
 
1.5%
936
4.8%
854
7.2%
779
10.6%
682
11.0%
570
9.4%
440
5.4%
318
 
2.4%
25
 
0.7%
13
 
0.4%

result
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)16.7%
Missing734
Missing (%)98.4%
Memory size6.0 KiB
tie
no result

Length

Max length9
Median length3
Mean length5
Min length3

Characters and Unicode

Total characters60
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno result
2nd rowtie
3rd rowno result
4th rowno result
5th rowno result

Common Values

ValueCountFrequency (%)
tie8
 
1.1%
no result4
 
0.5%
(Missing)734
98.4%

Length

2021-09-02T21:21:19.663696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-02T21:21:19.728880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
tie8
50.0%
no4
25.0%
result4
25.0%

Most occurring characters

ValueCountFrequency (%)
e12
20.0%
t12
20.0%
i8
13.3%
n4
 
6.7%
o4
 
6.7%
4
 
6.7%
r4
 
6.7%
s4
 
6.7%
u4
 
6.7%
l4
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter56
93.3%
Space Separator4
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e12
21.4%
t12
21.4%
i8
14.3%
n4
 
7.1%
o4
 
7.1%
r4
 
7.1%
s4
 
7.1%
u4
 
7.1%
l4
 
7.1%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin56
93.3%
Common4
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e12
21.4%
t12
21.4%
i8
14.3%
n4
 
7.1%
o4
 
7.1%
r4
 
7.1%
s4
 
7.1%
u4
 
7.1%
l4
 
7.1%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII60
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e12
20.0%
t12
20.0%
i8
13.3%
n4
 
6.7%
o4
 
6.7%
4
 
6.7%
r4
 
6.7%
s4
 
6.7%
u4
 
6.7%
l4
 
6.7%

overs
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
20
746 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1492
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20746
100.0%

Length

2021-09-02T21:21:19.881197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-02T21:21:19.941586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
20746
100.0%

Most occurring characters

ValueCountFrequency (%)
2746
50.0%
0746
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1492
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2746
50.0%
0746
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1492
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2746
50.0%
0746
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2746
50.0%
0746
50.0%

player_of_match
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)30.1%
Missing4
Missing (%)0.5%
Memory size6.0 KiB
CH Gayle
 
21
AB de Villiers
 
20
RG Sharma
 
17
DA Warner
 
17
MS Dhoni
 
17
Other values (218)
650 

Length

Max length17
Median length9
Mean length9.455525606
Min length5

Characters and Unicode

Total characters7016
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)12.7%

Sample

1st rowA Kumble
2nd rowDA Warner
3rd rowSM Pollock
4th rowAB de Villiers
5th rowDE Bollinger

Common Values

ValueCountFrequency (%)
CH Gayle21
 
2.8%
AB de Villiers20
 
2.7%
RG Sharma17
 
2.3%
DA Warner17
 
2.3%
MS Dhoni17
 
2.3%
YK Pathan16
 
2.1%
SR Watson15
 
2.0%
SK Raina14
 
1.9%
G Gambhir13
 
1.7%
MEK Hussey12
 
1.6%
Other values (213)580
77.7%

Length

2021-09-02T21:21:20.164854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sharma29
 
1.9%
a25
 
1.7%
sr23
 
1.5%
ch23
 
1.5%
v23
 
1.5%
de23
 
1.5%
ab22
 
1.5%
gayle21
 
1.4%
da20
 
1.3%
sk20
 
1.3%
Other values (344)1285
84.9%

Most occurring characters

ValueCountFrequency (%)
772
 
11.0%
a772
 
11.0%
e339
 
4.8%
n336
 
4.8%
r325
 
4.6%
i323
 
4.6%
h315
 
4.5%
l298
 
4.2%
S274
 
3.9%
s203
 
2.9%
Other values (43)3059
43.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4128
58.8%
Uppercase Letter2112
30.1%
Space Separator772
 
11.0%
Dash Punctuation4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a772
18.7%
e339
 
8.2%
n336
 
8.1%
r325
 
7.9%
i323
 
7.8%
h315
 
7.6%
l298
 
7.2%
s203
 
4.9%
t160
 
3.9%
o159
 
3.9%
Other values (16)898
21.8%
Uppercase Letter
ValueCountFrequency (%)
S274
13.0%
A191
 
9.0%
M186
 
8.8%
R172
 
8.1%
K158
 
7.5%
P142
 
6.7%
D124
 
5.9%
J106
 
5.0%
G100
 
4.7%
H99
 
4.7%
Other values (15)560
26.5%
Space Separator
ValueCountFrequency (%)
772
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6240
88.9%
Common776
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a772
 
12.4%
e339
 
5.4%
n336
 
5.4%
r325
 
5.2%
i323
 
5.2%
h315
 
5.0%
l298
 
4.8%
S274
 
4.4%
s203
 
3.3%
A191
 
3.1%
Other values (41)2864
45.9%
Common
ValueCountFrequency (%)
772
99.5%
-4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
772
 
11.0%
a772
 
11.0%
e339
 
4.8%
n336
 
4.8%
r325
 
4.6%
i323
 
4.6%
h315
 
4.5%
l298
 
4.2%
S274
 
3.9%
s203
 
2.9%
Other values (43)3059
43.6%

venue
Categorical

HIGH CORRELATION

Distinct36
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Eden Gardens
77 
Feroz Shah Kotla
73 
Wankhede Stadium
71 
M Chinnaswamy Stadium
66 
Rajiv Gandhi International Stadium, Uppal
63 
Other values (31)
396 

Length

Max length52
Median length21
Mean length24.98257373
Min length8

Characters and Unicode

Total characters18637
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDr DY Patil Sports Academy
2nd rowFeroz Shah Kotla
3rd rowWankhede Stadium
4th rowMaharashtra Cricket Association Stadium
5th rowDr DY Patil Sports Academy

Common Values

ValueCountFrequency (%)
Eden Gardens77
 
10.3%
Feroz Shah Kotla73
 
9.8%
Wankhede Stadium71
 
9.5%
M Chinnaswamy Stadium66
 
8.8%
Rajiv Gandhi International Stadium, Uppal63
 
8.4%
MA Chidambaram Stadium, Chepauk56
 
7.5%
Sawai Mansingh Stadium47
 
6.3%
Punjab Cricket Association Stadium, Mohali35
 
4.7%
Maharashtra Cricket Association Stadium21
 
2.8%
Punjab Cricket Association IS Bindra Stadium, Mohali19
 
2.5%
Other values (26)218
29.2%

Length

2021-09-02T21:21:20.410805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadium526
20.9%
cricket130
 
5.2%
association97
 
3.9%
international83
 
3.3%
eden77
 
3.1%
gardens77
 
3.1%
kotla73
 
2.9%
shah73
 
2.9%
feroz73
 
2.9%
wankhede71
 
2.8%
Other values (65)1234
49.1%

Most occurring characters

ValueCountFrequency (%)
a2446
 
13.1%
1768
 
9.5%
i1435
 
7.7%
t1127
 
6.0%
n1021
 
5.5%
d998
 
5.4%
e862
 
4.6%
S812
 
4.4%
r781
 
4.2%
m771
 
4.1%
Other values (43)6616
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13884
74.5%
Uppercase Letter2733
 
14.7%
Space Separator1768
 
9.5%
Other Punctuation241
 
1.3%
Dash Punctuation11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2446
17.6%
i1435
10.3%
t1127
 
8.1%
n1021
 
7.4%
d998
 
7.2%
e862
 
6.2%
r781
 
5.6%
m771
 
5.6%
h725
 
5.2%
u703
 
5.1%
Other values (15)3015
21.7%
Uppercase Letter
ValueCountFrequency (%)
S812
29.7%
C357
13.1%
M269
 
9.8%
A210
 
7.7%
G151
 
5.5%
P118
 
4.3%
R102
 
3.7%
I102
 
3.7%
K88
 
3.2%
W79
 
2.9%
Other values (13)445
16.3%
Other Punctuation
ValueCountFrequency (%)
,188
78.0%
.46
 
19.1%
'7
 
2.9%
Space Separator
ValueCountFrequency (%)
1768
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16617
89.2%
Common2020
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2446
14.7%
i1435
 
8.6%
t1127
 
6.8%
n1021
 
6.1%
d998
 
6.0%
e862
 
5.2%
S812
 
4.9%
r781
 
4.7%
m771
 
4.6%
h725
 
4.4%
Other values (38)5639
33.9%
Common
ValueCountFrequency (%)
1768
87.5%
,188
 
9.3%
.46
 
2.3%
-11
 
0.5%
'7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII18637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2446
 
13.1%
1768
 
9.5%
i1435
 
7.7%
t1127
 
6.0%
n1021
 
5.5%
d998
 
5.4%
e862
 
4.6%
S812
 
4.4%
r781
 
4.2%
m771
 
4.1%
Other values (43)6616
35.5%

umpire1
Categorical

HIGH CORRELATION

Distinct49
Distinct (%)6.6%
Missing1
Missing (%)0.1%
Memory size6.0 KiB
HDPK Dharmasena
78 
Asad Rauf
51 
AK Chaudhary
 
47
M Erasmus
 
40
Aleem Dar
 
38
Other values (44)
491 

Length

Max length21
Median length10
Mean length10.66845638
Min length5

Characters and Unicode

Total characters7948
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st rowRE Koertzen
2nd rowSS Hazare
3rd rowBR Doctrove
4th rowCB Gaffaney
5th rowBR Doctrove

Common Values

ValueCountFrequency (%)
HDPK Dharmasena78
 
10.5%
Asad Rauf51
 
6.8%
AK Chaudhary47
 
6.3%
M Erasmus40
 
5.4%
Aleem Dar38
 
5.1%
BF Bowden37
 
5.0%
S Ravi36
 
4.8%
BR Doctrove34
 
4.6%
AY Dandekar24
 
3.2%
KN Ananthapadmanabhan23
 
3.1%
Other values (39)337
45.2%

Length

2021-09-02T21:21:20.674582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dharmasena78
 
5.2%
hdpk78
 
5.2%
s65
 
4.3%
asad51
 
3.4%
rauf51
 
3.4%
chaudhary47
 
3.1%
ak47
 
3.1%
erasmus40
 
2.7%
m40
 
2.7%
aleem38
 
2.5%
Other values (83)968
64.4%

Most occurring characters

ValueCountFrequency (%)
a1006
 
12.7%
758
 
9.5%
n519
 
6.5%
e498
 
6.3%
r448
 
5.6%
h319
 
4.0%
D310
 
3.9%
s297
 
3.7%
d272
 
3.4%
A251
 
3.2%
Other values (37)3270
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5020
63.2%
Uppercase Letter2170
27.3%
Space Separator758
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1006
20.0%
n519
10.3%
e498
9.9%
r448
8.9%
h319
 
6.4%
s297
 
5.9%
d272
 
5.4%
o237
 
4.7%
m201
 
4.0%
u184
 
3.7%
Other values (14)1039
20.7%
Uppercase Letter
ValueCountFrequency (%)
D310
14.3%
A251
11.6%
K239
11.0%
B189
8.7%
R180
 
8.3%
S128
 
5.9%
C119
 
5.5%
H115
 
5.3%
N92
 
4.2%
J87
 
4.0%
Other values (12)460
21.2%
Space Separator
ValueCountFrequency (%)
758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7190
90.5%
Common758
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1006
 
14.0%
n519
 
7.2%
e498
 
6.9%
r448
 
6.2%
h319
 
4.4%
D310
 
4.3%
s297
 
4.1%
d272
 
3.8%
A251
 
3.5%
K239
 
3.3%
Other values (36)3031
42.2%
Common
ValueCountFrequency (%)
758
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1006
 
12.7%
758
 
9.5%
n519
 
6.5%
e498
 
6.3%
r448
 
5.6%
h319
 
4.0%
D310
 
3.9%
s297
 
3.7%
d272
 
3.4%
A251
 
3.2%
Other values (37)3270
41.1%

umpire2
Categorical

HIGH CORRELATION

Distinct48
Distinct (%)6.4%
Missing1
Missing (%)0.1%
Memory size6.0 KiB
S Ravi
68 
C Shamshuddin
56 
SJA Taufel
54 
CK Nandan
48 
RJ Tucker
 
41
Other values (43)
478 

Length

Max length15
Median length10
Mean length10.02281879
Min length5

Characters and Unicode

Total characters7467
Distinct characters48
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.7%

Sample

1st rowSJA Taufel
2nd rowSJA Taufel
3rd rowDJ Harper
4th rowVK Sharma
5th rowRB Tiffin

Common Values

ValueCountFrequency (%)
S Ravi68
 
9.1%
C Shamshuddin56
 
7.5%
SJA Taufel54
 
7.2%
CK Nandan48
 
6.4%
RJ Tucker41
 
5.5%
BNJ Oxenford31
 
4.2%
RB Tiffin30
 
4.0%
VA Kulkarni29
 
3.9%
AK Chaudhary29
 
3.9%
Nitin Menon27
 
3.6%
Other values (38)332
44.5%

Length

2021-09-02T21:21:20.918739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s84
 
5.6%
ravi68
 
4.5%
shamshuddin56
 
3.7%
c56
 
3.7%
taufel54
 
3.6%
sja54
 
3.6%
nandan48
 
3.2%
ck48
 
3.2%
rj41
 
2.7%
tucker41
 
2.7%
Other values (78)951
63.4%

Most occurring characters

ValueCountFrequency (%)
a771
 
10.3%
756
 
10.1%
n473
 
6.3%
r415
 
5.6%
e373
 
5.0%
i370
 
5.0%
S321
 
4.3%
h310
 
4.2%
u251
 
3.4%
d245
 
3.3%
Other values (38)3182
42.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4593
61.5%
Uppercase Letter2118
28.4%
Space Separator756
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a771
16.8%
n473
10.3%
r415
9.0%
e373
 
8.1%
i370
 
8.1%
h310
 
6.7%
u251
 
5.5%
d245
 
5.3%
f183
 
4.0%
s180
 
3.9%
Other values (15)1022
22.3%
Uppercase Letter
ValueCountFrequency (%)
S321
15.2%
K228
10.8%
R196
9.3%
A181
8.5%
J172
8.1%
T156
 
7.4%
C150
 
7.1%
N133
 
6.3%
B90
 
4.2%
M77
 
3.6%
Other values (12)414
19.5%
Space Separator
ValueCountFrequency (%)
756
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6711
89.9%
Common756
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a771
 
11.5%
n473
 
7.0%
r415
 
6.2%
e373
 
5.6%
i370
 
5.5%
S321
 
4.8%
h310
 
4.6%
u251
 
3.7%
d245
 
3.7%
K228
 
3.4%
Other values (37)2954
44.0%
Common
ValueCountFrequency (%)
756
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a771
 
10.3%
756
 
10.1%
n473
 
6.3%
r415
 
5.6%
e373
 
5.0%
i370
 
5.0%
S321
 
4.3%
h310
 
4.2%
u251
 
3.4%
d245
 
3.3%
Other values (38)3182
42.6%

umpire3
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing744
Missing (%)99.7%
Memory size6.0 KiB
S Ravi
A Chaudary

Length

Max length10
Median length8
Mean length8
Min length6

Characters and Unicode

Total characters16
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowA Chaudary
2nd rowS Ravi

Common Values

ValueCountFrequency (%)
S Ravi1
 
0.1%
A Chaudary1
 
0.1%
(Missing)744
99.7%

Length

2021-09-02T21:21:21.115178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-02T21:21:21.182220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
chaudary1
25.0%
s1
25.0%
a1
25.0%
ravi1
25.0%

Most occurring characters

ValueCountFrequency (%)
a3
18.8%
2
12.5%
A1
 
6.2%
C1
 
6.2%
h1
 
6.2%
u1
 
6.2%
d1
 
6.2%
r1
 
6.2%
y1
 
6.2%
S1
 
6.2%
Other values (3)3
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10
62.5%
Uppercase Letter4
 
25.0%
Space Separator2
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3
30.0%
h1
 
10.0%
u1
 
10.0%
d1
 
10.0%
r1
 
10.0%
y1
 
10.0%
v1
 
10.0%
i1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
A1
25.0%
C1
25.0%
S1
25.0%
R1
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14
87.5%
Common2
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3
21.4%
A1
 
7.1%
C1
 
7.1%
h1
 
7.1%
u1
 
7.1%
d1
 
7.1%
r1
 
7.1%
y1
 
7.1%
S1
 
7.1%
R1
 
7.1%
Other values (2)2
14.3%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a3
18.8%
2
12.5%
A1
 
6.2%
C1
 
6.2%
h1
 
6.2%
u1
 
6.2%
d1
 
6.2%
r1
 
6.2%
y1
 
6.2%
S1
 
6.2%
Other values (3)3
18.8%

first_bat_team
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Mumbai Indians
99 
Kings XI Punjab
90 
Chennai Super Kings
87 
Royal Challengers Bangalore
85 
Kolkata Knight Riders
82 
Other values (10)
303 

Length

Max length27
Median length16
Mean length17.97184987
Min length13

Characters and Unicode

Total characters13407
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeccan Chargers
2nd rowDelhi Daredevils
3rd rowKolkata Knight Riders
4th rowRoyal Challengers Bangalore
5th rowChennai Super Kings

Common Values

ValueCountFrequency (%)
Mumbai Indians99
13.3%
Kings XI Punjab90
12.1%
Chennai Super Kings87
11.7%
Royal Challengers Bangalore85
11.4%
Kolkata Knight Riders82
11.0%
Delhi Daredevils72
9.7%
Rajasthan Royals66
8.8%
Sunrisers Hyderabad61
8.2%
Deccan Chargers43
5.8%
Pune Warriors20
 
2.7%
Other values (5)41
5.5%

Length

2021-09-02T21:21:21.356494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings177
 
9.5%
mumbai99
 
5.3%
indians99
 
5.3%
punjab90
 
4.8%
xi90
 
4.8%
super87
 
4.7%
chennai87
 
4.7%
bangalore85
 
4.6%
challengers85
 
4.6%
royal85
 
4.6%
Other values (22)874
47.0%

Most occurring characters

ValueCountFrequency (%)
a1510
 
11.3%
n1140
 
8.5%
1112
 
8.3%
e1004
 
7.5%
i927
 
6.9%
s887
 
6.6%
r783
 
5.8%
l649
 
4.8%
g502
 
3.7%
h447
 
3.3%
Other values (27)4446
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10347
77.2%
Uppercase Letter1948
 
14.5%
Space Separator1112
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1510
14.6%
n1140
11.0%
e1004
9.7%
i927
9.0%
s887
8.6%
r783
 
7.6%
l649
 
6.3%
g502
 
4.9%
h447
 
4.3%
u408
 
3.9%
Other values (11)2090
20.2%
Uppercase Letter
ValueCountFrequency (%)
K355
18.2%
R314
16.1%
C220
11.3%
D192
9.9%
I189
9.7%
S163
8.4%
P125
 
6.4%
M99
 
5.1%
X90
 
4.6%
B85
 
4.4%
Other values (5)116
 
6.0%
Space Separator
ValueCountFrequency (%)
1112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12295
91.7%
Common1112
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1510
 
12.3%
n1140
 
9.3%
e1004
 
8.2%
i927
 
7.5%
s887
 
7.2%
r783
 
6.4%
l649
 
5.3%
g502
 
4.1%
h447
 
3.6%
u408
 
3.3%
Other values (26)4038
32.8%
Common
ValueCountFrequency (%)
1112
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1510
 
11.3%
n1140
 
8.5%
1112
 
8.3%
e1004
 
7.5%
i927
 
6.9%
s887
 
6.6%
r783
 
5.8%
l649
 
4.8%
g502
 
3.7%
h447
 
3.3%
Other values (27)4446
33.2%

first_bowl_team
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Royal Challengers Bangalore
94 
Kolkata Knight Riders
94 
Delhi Daredevils
89 
Mumbai Indians
84 
Kings XI Punjab
84 
Other values (10)
301 

Length

Max length27
Median length16
Mean length18.08310992
Min length13

Characters and Unicode

Total characters13490
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowKolkata Knight Riders
3rd rowMumbai Indians
4th rowRising Pune Supergiants
5th rowDeccan Chargers

Common Values

ValueCountFrequency (%)
Royal Challengers Bangalore94
12.6%
Kolkata Knight Riders94
12.6%
Delhi Daredevils89
11.9%
Mumbai Indians84
11.3%
Kings XI Punjab84
11.3%
Rajasthan Royals80
10.7%
Chennai Super Kings73
9.8%
Sunrisers Hyderabad44
5.9%
Deccan Chargers32
 
4.3%
Pune Warriors26
 
3.5%
Other values (5)46
6.2%

Length

2021-09-02T21:21:21.568158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings157
 
8.4%
delhi97
 
5.2%
royal94
 
5.1%
kolkata94
 
5.1%
bangalore94
 
5.1%
challengers94
 
5.1%
knight94
 
5.1%
riders94
 
5.1%
daredevils89
 
4.8%
mumbai84
 
4.5%
Other values (22)868
46.7%

Most occurring characters

ValueCountFrequency (%)
a1553
 
11.5%
1113
 
8.3%
n1080
 
8.0%
e1019
 
7.6%
i918
 
6.8%
s884
 
6.6%
r763
 
5.7%
l751
 
5.6%
g501
 
3.7%
h477
 
3.5%
Other values (27)4431
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10434
77.3%
Uppercase Letter1943
 
14.4%
Space Separator1113
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1553
14.9%
n1080
10.4%
e1019
9.8%
i918
8.8%
s884
8.5%
r763
 
7.3%
l751
 
7.2%
g501
 
4.8%
h477
 
4.6%
o411
 
3.9%
Other values (11)2077
19.9%
Uppercase Letter
ValueCountFrequency (%)
R363
18.7%
K359
18.5%
D218
11.2%
C207
10.7%
I168
8.6%
S132
 
6.8%
P125
 
6.4%
B94
 
4.8%
M84
 
4.3%
X84
 
4.3%
Other values (5)109
 
5.6%
Space Separator
ValueCountFrequency (%)
1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12377
91.7%
Common1113
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1553
 
12.5%
n1080
 
8.7%
e1019
 
8.2%
i918
 
7.4%
s884
 
7.1%
r763
 
6.2%
l751
 
6.1%
g501
 
4.0%
h477
 
3.9%
o411
 
3.3%
Other values (26)4020
32.5%
Common
ValueCountFrequency (%)
1113
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1553
 
11.5%
1113
 
8.3%
n1080
 
8.0%
e1019
 
7.6%
i918
 
6.8%
s884
 
6.6%
r763
 
5.7%
l751
 
5.6%
g501
 
3.7%
h477
 
3.5%
Other values (27)4431
32.8%

first_bat_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct148
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.3565684
Minimum56
Maximum263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2021-09-02T21:21:21.678642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile111
Q1142
median163
Q3182
95-th percentile209.75
Maximum263
Range207
Interquartile range (IQR)40

Descriptive statistics

Standard deviation30.53784434
Coefficient of variation (CV)0.1892569026
Kurtosis0.4992793235
Mean161.3565684
Median Absolute Deviation (MAD)20
Skewness-0.200546894
Sum120372
Variance932.5599367
MonotonicityNot monotonic
2021-09-02T21:21:21.792281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18716
 
2.1%
16515
 
2.0%
16014
 
1.9%
17814
 
1.9%
16814
 
1.9%
16314
 
1.9%
16413
 
1.7%
18312
 
1.6%
16112
 
1.6%
14812
 
1.6%
Other values (138)610
81.8%
ValueCountFrequency (%)
561
0.1%
621
0.1%
672
0.3%
702
0.3%
731
0.1%
801
0.1%
811
0.1%
821
0.1%
881
0.1%
891
0.1%
ValueCountFrequency (%)
2631
 
0.1%
2481
 
0.1%
2461
 
0.1%
2451
 
0.1%
2401
 
0.1%
2351
 
0.1%
2322
0.3%
2313
0.4%
2301
 
0.1%
2271
 
0.1%

second_bat_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct143
Distinct (%)19.2%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean147.8413978
Minimum2
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2021-09-02T21:21:21.908636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile92
Q1131
median150
Q3169
95-th percentile194
Maximum223
Range221
Interquartile range (IQR)38

Descriptive statistics

Standard deviation31.15340597
Coefficient of variation (CV)0.2107218034
Kurtosis0.9115891677
Mean147.8413978
Median Absolute Deviation (MAD)19
Skewness-0.6126276321
Sum109994
Variance970.5347038
MonotonicityNot monotonic
2021-09-02T21:21:22.022704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16414
 
1.9%
16114
 
1.9%
14614
 
1.9%
16214
 
1.9%
14213
 
1.7%
16013
 
1.7%
15913
 
1.7%
15013
 
1.7%
13712
 
1.6%
15311
 
1.5%
Other values (133)613
82.2%
ValueCountFrequency (%)
21
0.1%
411
0.1%
441
0.1%
481
0.1%
491
0.1%
551
0.1%
582
0.3%
601
0.1%
611
0.1%
662
0.3%
ValueCountFrequency (%)
2232
0.3%
2171
 
0.1%
2142
0.3%
2111
 
0.1%
2082
0.3%
2072
0.3%
2062
0.3%
2051
 
0.1%
2043
0.4%
2031
 
0.1%

Interactions

2021-09-02T21:21:10.706802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:10.877079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.008719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.123275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.221603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.329041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.437610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.558889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.673795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.783149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.885119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:11.997828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:12.752070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:12.856917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:12.962059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.067820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.151761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.259248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.362422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.450488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.555685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.632256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.717063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.811588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:13.908816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.006541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.105143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.211853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.314719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.408826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.516606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.614795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.722251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.824832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:14.930229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-02T21:21:15.035915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-02T21:21:22.122798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-02T21:21:22.263292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-02T21:21:22.625956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-02T21:21:22.794424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-02T21:21:15.267306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-02T21:21:15.717873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-02T21:21:16.072028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-02T21:21:16.278406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idseasoncitydateteam1team2toss_winnertoss_decisionwinnereliminatordl_appliedwin_by_runswin_by_wicketsresultoversplayer_of_matchvenueumpire1umpire2umpire3first_bat_teamfirst_bowl_teamfirst_bat_scoresecond_bat_score
04191642010Mumbai2010-04-24Royal Challengers BangaloreDeccan ChargersDeccan ChargersbatRoyal Challengers BangaloreNaNNaNNaN9.0NaN20A KumbleDr DY Patil Sports AcademyRE KoertzenSJA TaufelNaNDeccan ChargersRoyal Challengers Bangalore82.086.0
14191312010Delhi2010-03-29Delhi DaredevilsKolkata Knight RidersDelhi DaredevilsbatDelhi DaredevilsNaNNaN40.0NaNNaN20DA WarnerFeroz Shah KotlaSS HazareSJA TaufelNaNDelhi DaredevilsKolkata Knight Riders177.0137.0
23360212008Mumbai2008-05-16Mumbai IndiansKolkata Knight RidersMumbai IndiansfieldMumbai IndiansNaNNaNNaN8.0NaN20SM PollockWankhede StadiumBR DoctroveDJ HarperNaNKolkata Knight RidersMumbai Indians67.068.0
39809312016Pune2016-04-22Rising Pune SupergiantsRoyal Challengers BangaloreRising Pune SupergiantsfieldRoyal Challengers BangaloreNaNNaN13.0NaNNaN20AB de VilliersMaharashtra Cricket Association StadiumCB GaffaneyVK SharmaNaNRoyal Challengers BangaloreRising Pune Supergiants185.0172.0
44191632010Mumbai2010-04-22Chennai Super KingsDeccan ChargersChennai Super KingsbatChennai Super KingsNaNNaN38.0NaNNaN20DE BollingerDr DY Patil Sports AcademyBR DoctroveRB TiffinNaNChennai Super KingsDeccan Chargers142.0104.0
54191362010Chandigarh2010-04-02Kings XI PunjabRoyal Challengers BangaloreKings XI PunjabbatRoyal Challengers BangaloreNaNNaNNaN6.0NaN20KP PietersenPunjab Cricket Association Stadium, MohaliBF BowdenM ErasmusNaNKings XI PunjabRoyal Challengers Bangalore181.0184.0
63360262008Bangalore2008-05-19Royal Challengers BangaloreDelhi DaredevilsDelhi DaredevilsfieldDelhi DaredevilsNaNNaNNaN5.0NaN20SP GoswamiM Chinnaswamy StadiumSJ DavisGA PratapkumarNaNRoyal Challengers BangaloreDelhi Daredevils154.0158.0
79809632016Kolkata2016-05-04Kolkata Knight RidersKings XI PunjabKings XI PunjabfieldKolkata Knight RidersNaNNaN7.0NaNNaN20AD RussellEden GardensAK ChaudharyHDPK DharmasenaNaNKolkata Knight RidersKings XI Punjab164.0157.0
85012362011Chennai2011-05-01Chennai Super KingsDeccan ChargersChennai Super KingsbatChennai Super KingsNaNNaN19.0NaNNaN20JA MorkelMA Chidambaram Stadium, ChepaukAleem DarRB TiffinNaNChennai Super KingsDeccan Chargers165.0146.0
95012632011Mumbai2011-05-20Mumbai IndiansRajasthan RoyalsMumbai IndiansbatRajasthan RoyalsNaNNaNNaN10.0NaN20SR WatsonWankhede StadiumRE KoertzenPR ReiffelNaNMumbai IndiansRajasthan Royals133.0134.0

Last rows

idseasoncitydateteam1team2toss_winnertoss_decisionwinnereliminatordl_appliedwin_by_runswin_by_wicketsresultoversplayer_of_matchvenueumpire1umpire2umpire3first_bat_teamfirst_bowl_teamfirst_bat_scoresecond_bat_score
7363360152008Jaipur2008-05-11Rajasthan RoyalsDelhi DaredevilsRajasthan RoyalsfieldRajasthan RoyalsNaNNaNNaN3.0NaN20SR WatsonSawai Mansingh StadiumSJ DavisRE KoertzenNaNDelhi DaredevilsRajasthan Royals156.0159.0
7374191502010Jaipur2010-04-11Rajasthan RoyalsMumbai IndiansRajasthan RoyalsfieldMumbai IndiansNaNNaN37.0NaNNaN20SR TendulkarSawai Mansingh StadiumBR DoctroveSK TaraporeNaNMumbai IndiansRajasthan Royals174.0137.0
7389809572016Pune2016-05-01Rising Pune SupergiantsMumbai IndiansMumbai IndiansfieldMumbai IndiansNaNNaNNaN8.0NaN20RG SharmaMaharashtra Cricket Association StadiumAY DandekarRJ TuckerNaNRising Pune SupergiantsMumbai Indians159.0161.0
73911365982018Indore2018-05-06Kings XI PunjabRajasthan RoyalsKings XI PunjabfieldKings XI PunjabNaNNaNNaN6.0NaN20Mujeeb Ur RahmanHolkar Cricket StadiumC ShamshuddinS RaviNaNRajasthan RoyalsKings XI Punjab152.0155.0
7409810092016Kolkata2016-05-22Kolkata Knight RidersSunrisers HyderabadSunrisers HyderabadfieldKolkata Knight RidersNaNNaN22.0NaNNaN20YK PathanEden GardensKN AnanthapadmanabhanM ErasmusNaNKolkata Knight RidersSunrisers Hyderabad171.0149.0
74111365672018Hyderabad2018-04-12Sunrisers HyderabadMumbai IndiansSunrisers HyderabadfieldSunrisers HyderabadNaNNaNNaN1.0NaN20Rashid KhanRajiv Gandhi International Stadium, UppalNJ LlongCK NandanNaNMumbai IndiansSunrisers Hyderabad147.0151.0
7423922092009East London2009-05-04Chennai Super KingsDeccan ChargersChennai Super KingsbatChennai Super KingsNaNNaN78.0NaNNaN20MS DhoniBuffalo ParkBR DoctroveM ErasmusNaNChennai Super KingsDeccan Chargers178.0100.0
7433360122008Bangalore2008-05-28Royal Challengers BangaloreMumbai IndiansMumbai IndiansfieldMumbai IndiansNaNNaNNaN9.0NaN20CRD FernandoM Chinnaswamy StadiumBF BowdenAV JayaprakashNaNRoyal Challengers BangaloreMumbai Indians122.0126.0
7447293132014NaN2014-04-28Kings XI PunjabRoyal Challengers BangaloreKings XI PunjabfieldKings XI PunjabNaNNaNNaN5.0NaN20Sandeep SharmaDubai International Cricket StadiumBF BowdenS RaviNaNRoyal Challengers BangaloreKings XI Punjab124.0127.0
7454191572010Bangalore2010-04-17Royal Challengers BangaloreMumbai IndiansRoyal Challengers BangalorefieldMumbai IndiansNaNNaN57.0NaNNaN20R McLarenM Chinnaswamy StadiumHDPK DharmasenaSJA TaufelNaNMumbai IndiansRoyal Challengers Bangalore191.0134.0